{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aaf9e85f-9588-455f-a554-f7a82d3c8c21",
   "metadata": {},
   "source": [
    "# 计算机中数的表示\n",
    "\n",
    "16G内存可以存放多少个字符？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b6702eb-4ff9-4a6a-afbb-f92c83e6ec3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17179869184\n"
     ]
    }
   ],
   "source": [
    "print(16*1024*1024*1024)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee97fa2b-2068-47a6-9815-d1f10f837266",
   "metadata": {},
   "source": [
    "请问：4378点*4378点的图片要多少M内存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "105e79d5-05d3-4e4f-a4a7-be30370bb31f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54.83689498901367\n"
     ]
    }
   ],
   "source": [
    "a=4378*4378*3/1024/1024\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "46a78820-c0b9-4345-b5bc-824c389f203d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "298.7769457640237\n"
     ]
    }
   ],
   "source": [
    "print(16*1024/a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f2d638c-4834-40f1-a91d-9a9080601c7f",
   "metadata": {},
   "source": [
    "# 快速生成文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dcbba7a1-c262-48f1-b616-011fc0e11507",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60ebb1cc-1f2d-4693-bb58-2601091ab7ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting velocity_earth.csv\n"
     ]
    }
   ],
   "source": [
    "%%writefile velocity_earth.csv\n",
    "name,velocity,earth\n",
    "苹果,0.1,falldown\n",
    "网球,50,falldown\n",
    "羽毛球,63,falldown\n",
    "大炮,600,falldown\n",
    "子弹,850,falldown\n",
    "鬼怪,2370,falldown\n",
    "防空导弹,3000,falldown\n",
    "神7,7820,flyout\n",
    "新视野号,16260.0,flyout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b6dcbf12-9161-437a-9f34-f6cc95611264",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('velocity_earth.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "69c637f2-120e-4271-b740-01dca6323019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>velocity</th>\n",
       "      <th>earth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>苹果</td>\n",
       "      <td>0.1</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>网球</td>\n",
       "      <td>50.0</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>羽毛球</td>\n",
       "      <td>63.0</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大炮</td>\n",
       "      <td>600.0</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>子弹</td>\n",
       "      <td>850.0</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>鬼怪</td>\n",
       "      <td>2370.0</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>防空导弹</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>神7</td>\n",
       "      <td>7820.0</td>\n",
       "      <td>flyout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>新视野号</td>\n",
       "      <td>16260.0</td>\n",
       "      <td>flyout</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  velocity     earth\n",
       "0    苹果       0.1  falldown\n",
       "1    网球      50.0  falldown\n",
       "2   羽毛球      63.0  falldown\n",
       "3    大炮     600.0  falldown\n",
       "4    子弹     850.0  falldown\n",
       "5    鬼怪    2370.0  falldown\n",
       "6  防空导弹    3000.0  falldown\n",
       "7    神7    7820.0    flyout\n",
       "8  新视野号   16260.0    flyout"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "230443f4-43c3-43de-a204-38c22812e972",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      子弹\n",
       "5      鬼怪\n",
       "6    防空导弹\n",
       "7      神7\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'][4:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c8a489f1-0705-46d2-ae79-f38bd36108dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "earth\n",
       "falldown    7\n",
       "flyout      2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['earth'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "13db394b-5215-46a0-8ff4-60e405eb7734",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e36d2ff9-bfef-44e9-9606-071818857624",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting Year_Money.csv\n"
     ]
    }
   ],
   "source": [
    "%%writefile Year_Money.csv\n",
    "Year,Money\n",
    "1990,50\n",
    "1991,52\n",
    "1992,55\n",
    "1993,57\n",
    "1994,61\n",
    "1995,67\n",
    "1996,71\n",
    "1997,73\n",
    "1998,75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a83c52e-7590-47d4-a070-d1001bc95a14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Money</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1991</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1995</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1996</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1997</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1998</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Money\n",
       "0  1990     50\n",
       "1  1991     52\n",
       "2  1992     55\n",
       "3  1993     57\n",
       "4  1994     61\n",
       "5  1995     67\n",
       "6  1996     71\n",
       "7  1997     73\n",
       "8  1998     75"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd=pd.read_csv('Year_Money.csv')\n",
    "fd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8e848d60-0da1-4bdb-81f7-35d7dd53561d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "freqs_1=fd['Year']\n",
    "freqs_2=fd['Money']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ea768427-9b15-4188-b604-17a43c1a582c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zIiIiXujYMXjvPfPx++9mrFo1sx6mWzezS29xoTAjIiLiRfbvN7Mw48fD6dNm7Prrze3VHTqYRb7FjcKMiIiIF9i+3ayHmTwZ0tPNWIMGMGQIPPig+x/u6EkUZkRERDzYTz+ZO5Nmzjy3W++dd5rdelu08L7del1BYUZERMQDrVtn9oiZN+/cWKtW5nLSnXfaV5cnUpgRERHxEJYFy5aZEPPNN2bM4YB27UyIadDA3vo8lcKMiIiIzbKz4YsvTIhZu9aMBQRAp04waJBZ4CsXpzAjIiJik8xM+OQTsybmp5/MWMmS0KMHDBhgbrWWy1OYERERcYGsLEhOdrBiRRVKl3YQG3vujqO0NJgyBYYPhx07zFhoKDz9NPTtC5Ur21e3N1KYERERcbKkJBNKUlICgIaMHGmeiTR8uNml9513zH4xABUrQr9+JsiUK2dj0V5MYUZERMSJkpLMgl3LyjuekgIdO557XaUK/P3v0L07lC7t3hp9jcKMiIiIk2RlmRmZC4PM+QICYOxY6NwZgoLcV5sv87O7ABEREV+xcqWZgbmUzEyoVUtBxpkUZkRERJzku++u7LgDB1xbR3GjMCMiIlJEGzbAww+bNTBXIiLCtfUUN1ozIyIiUgiWZS4rDRsGixadGy9ZEv78s+CvcTjMXU16HIFzaWZGRETkKlgWfPWVCSQxMSbI+Pub3Xp/+gn+/W8TWi58AGTO69Gji/cTrl1BYUZEROQKZGWZ3XobNID774dvvzWLeHv3hm3b4OOP4YYbID4e5swxt16fr2pVMx4fb0/9vkyXmURERC4hPR2mTYO33jKhBaBMGRNinnuu4PUv8fHwwAOwbFkmCxZspFWr+sTGBmhGxkUUZkRERApw5gx8+CG8/fa5262vucbsI/O3v0H58pf+en9/iImxOH16HzExtyjIuJDCjIiIyHlOnjSb2o0aBUeOmLGICHj+eejZE0JC7K1P8lOYERERAQ4fhjFj4IMPIDXVjNWoAYMGQZcu5i4l8UwKMyIiUqzt3Wse/DhxIpw9a8ZuuAEGD4ZHHzWPHxDPpl8iEREpln791TzFeupUyMgwY7ffDkOGQJs24Kf7fb2GwoyIiBQr//2v2ehu9uxzD4SMjTUh5u678+8PI55PYUZERIqFVatMiPnyy3NjbdqYy0l33GFfXVJ0CjMiIuKzLAuWLDEhJjnZjPn5mbUwL7wAN99sb33iHAozIiLic7Kz4bPPTIjZsMGMBQZC164wcCBcd52d1YmzKcyIiIjPyMiAGTPMbr1btpix4GB46ino3988UkB8j8KMiIh4vbNnITERRoyA3bvNWNmyZqfeZ5+FSpXsrU9cS2FGRES81qlT8K9/wciRcOiQGQsLM7MwvXtDaKi99Yl7KMyIiIjXOXYM3nvPfPz+uxmrVs2sh+nWDUqVsrU8cTOFGRER8Rr79plZmPHj4fRpM3b99eb26g4dzCJfKX4UZkRExONt327Ww0yeDOnpZqxBA7PR3YMPoidSF3MKMyIi4rE2bTJ3Js2caW63BrjzTnjxRWjRQrv1iqEwIyIitsjKguRkBytWVKF0aQexsedmWNauNXvEzJ9/7vhWrczlpDvvtKde8VwKMyIi4nZJSdC3L6SkBAANGTnS7AHTvTusXAnffGOOczigXTsTYho0sLVk8WAKMyIi4lZJSSag5DzkMUdKCiQkmM8DAqBTJxg0yCzwFbkUhRkREXGbrCwzI3NhkDlfmTLw449Qo4b76hLv5md3ASIiUnysXGlmYC7ljz/O7eIrciUUZkRExG0OHHDucSKgMCMiIm50pc9IiohwbR3iW7RmRkRE3CItDd5//9LHOBzmribdfi1XQzMzIiLicmfOQNu2Zt+YgP/9M/rCDe9yXo8erR195eoozIiIiEulpkLLlrB4MZQuDQsXwqefQpUqeY+rWhXmzIH4eHvqFO+ly0wiIuIyx4+bILN+PYSGwoIF0KSJee+BB2DZskwWLNhIq1b1iY0N0IyMFIrCjIiIuMShQxAXZ56vVKGCmZm59dZz7/v7Q0yMxenT+4iJuUVBRgpNYUZERJwuJQXuuQd+/RXCw+Hrr+GGG+yuSnyVwoyIiDjVjh0myOzaBdWqmecsXXed3VWJL7N1AXD16tVxOBz5Pp5++mkALMsiISGByMhISpUqRbNmzdi8ebOdJYuIyCVs2WJuq961ywSYlSsVZMT1bA0z69ev58CBA7kfS5YsAaB9+/YAjBgxgpEjR/LBBx+wfv16wsPDiYuL49SpU3aWLSIiBdi4EWJiYP9+c0lpxQozMyPiaraGmUqVKhEeHp778cUXX1CzZk1iYmKwLIvRo0czZMgQ4uPjufHGG5kyZQpnzpxh+vTpdpYtIiIXWLMGYmPhyBGzyHf5cu3iK+7jMWtm0tPTmTZtGv3798fhcLBjxw4OHjxI8+bNc48JCgoiJiaGVatW8dRTTxV4nrS0NNLS0nJfp6amApCRkUFGRoZTa845n7PP6ynUn/fz9R7Vn2dITnbw0EP+/PGHgzvuyGb+/CzKloUrKdtbeiws9Vf0c18Jh2Vd6kHs7vPJJ5/QoUMH9uzZQ2RkJKtWraJp06bs27ePyMjI3ON69uzJ7t27WbRoUYHnSUhI4JVXXsk3Pn36dIKDg11Wv4hIcfT992G89db/kZ7uz803H+HFF9dSsmSW3WWJDzhz5gwdOnTg5MmThIaGXvJYj5mZmTRpEq1atcoTXAAcF+x3bVlWvrHzDR48mP79++e+Tk1NJSoqiubNm1/2h3G1MjIyWLJkCXFxcQQGBjr13J5A/Xk/X+9R/dlr7lwHb77pT0aGg/vuy2bmzHKULNniqs7h6T0WlforvJwrK1fCI8LM7t27+frrr0lKSsodCw8PB+DgwYNEnHfh9fDhw1SuXPmi5woKCiIoKCjfeGBgoMt+I7ny3J5A/Xk/X+9R/bnftGnQtStkZcEjj8C0aX4EBhZ+GaYn9uhM6q9w57xSHvFspsTERMLCwrj//vtzx2rUqEF4eHjuHU5g1tUkJyfTJGcvbBERcbsJE6BzZxNkunaF6dPBh/8/LV7A9pmZ7OxsEhMT6dKlCwEB58pxOBz069ePYcOGUatWLWrVqsWwYcMIDg6mQ4cONlYsIlJ8jRoFOVfyn34a3nsP/Dzin8VSnNkeZr7++mv27NlDt27d8r03cOBAzp49S58+fThx4gSNGjVi8eLFhISE2FCpiEjxZVnw+uvw8svm9cCB8NZbcIkljCJuY3uYad68ORe7ocrhcJCQkEBCQoJ7ixIRkVyWBS+8ACNGmNevvQZDhijIiOewPcyIiIjnys6GZ5+Ff/7TvB45Ep57zt6aRC6kMCMiIgXKzIQePWDyZDMLM24c9Oxpd1Ui+SnMiIhIPunp8MQTMHs2+PubQPPEE3ZXJVIwhRkREcnjzz+hfXv44gtzy/XMmRAfb3dVIhenMCMiIrn++AMeeACWLoWSJWHuXGjZ0u6qRC5NYUZERAA4eRLuuw9WrYIyZczMTEyM3VWJXJ7CjIiIcPQotGgB338P5crBggXQuLHdVYlcGYUZEZFi7sABiIuDzZuhUiVYvBjq17e7KpErpzAjIlKM7d4N994Lv/0GkZHw9ddQt67dVYlcHYUZEZFiats2uOce2LsXqleHb76Ba6+1uyqRq6fHg4mIFEObN8Ndd5kgU7s2rFihICPeS2FGRKSY2bDB3KV08CDcdJMJMlFRdlclUngKMyIixci338Ldd8OxY3D77bB8OVSubHdVIkWjMCMiUkx88w00bw6pqXDnnWaxb/nydlclUnQKMyIixcAXX8D998OZMybQLFwIoaF2VyXiHAozIiI+bvZseOghSEszjyqYPx+Cg+2uSsR5FGZERHzY5Mnw2GOQmQmPP26CTVCQ3VWJOJfCjIiIjxo7Fv76V8jOhu7dYepU8xRsEV+jMCMi4oNGjICnnzaf9+0LEyaAv7+9NYm4isKMiIgPsSx4+WUYNMi8fvFFGDUKHA576xJxJT3OQETER1gWDBgAI0ea18OGweDB9tYk4g4KMyIiPiA7G/r0gfHjzesxY+DZZ+2tScRdFGZERLxcZqZZ6Dttmrmc9OGH0K2b3VWJuI/CjIiIF0tLgw4dICnJLPCdNs3cii1SnCjMiIh4qbNnIT7e7OZbogR88onZFE+kuFGYERHxQqdOQZs2kJwMpUrBvHkQF2d3VSL2UJgREfEyJ05Aq1awdi2EhMCXX5oHR4oUVwozIiJe5PBh86DI//7XPPF64UK4/Xa7qxKxlzbNExHxQFlZkJzsYMWKKiQnO8jKgn37ICbGBJmwMFi+XEFGBDQzIyLicZKSzCMIUlICgIaMHAnh4WYvmcOHoWpV+OYbqF3b7kpFPIPCjIiIB0lKgnbtzG6+5zt40Py3cmVYuRKqV3d7aSIeS5eZREQ8RFaWmZG5MMicz98foqLcV5OIN1CYERHxECtXQkrKpY/Zv98cJyLnKMyIiHiIAwece5xIcaEwIyLiISIinHucSHGhMCMi4iHuvNPcqXQxDodZL6MN8kTyUpgREfEQ/v6QkFDwew6H+e/o0eY4ETlHYUZExENYFsyZYz4PDMz7XtWq5r34ePfXJeLptM+MiIiHGDvWPJ6gZElYtw4OHcpkwYKNtGpVn9jYAM3IiFyEwoyIiAf45RcYMMB8Pnw43HQT1Kljcfr0PmJiblGQEbkEXWYSEbFZejo88QT8+SfExcEzz9hdkYh3UZgREbHZq6/Chg1wzTWQmAh++ptZ5Kroj4yIiI1WrYI33zSfjx8PVarYW4+IN1KYERGxyalT0KmTeRp2p07Qvr3dFYl4J4UZERGb9OsHO3ZAtWrw/vt2VyPivRRmRERs8Nln8NFHZjO8jz+GsmXtrkjEeynMiIi42cGD0KOH+XzAAIiJsbceEW+nMCMi4kaWBU8+CUePwi23wGuv2V2RiPdTmBERcaPx4+GrryAoCKZNM/8VkaJRmBERcZNff4Xnnzefv/km3HijvfWI+AqFGRERN8jIMLv8njkD99wDffvaXZGI71CYERFxg9dfh/XroVw5mDxZu/yKOJP+OImIuNiaNfDGG+bzf/0Lqla1tx4RX6MwIyLiQn/8YS4vZWVBhw7w2GN2VyTiexRmRERcqH9/2L4doqLgn/+0uxoR36QwIyLiIvPnw8SJZpffKVPMehkRcT7bw8y+fft44oknqFChAsHBwdSvX58NGzbkvt+1a1ccDkeej8aNG9tYsYjI5R06BN27m8/794fYWHvrEfFlAXZ+8xMnTtC0aVNiY2NZsGABYWFhbN++nXIX/POlZcuWJCYm5r4uUaKEmysVEblylmWCzJEjcNNN5xb/iohr2Bpmhg8fTlRUVJ6gUr169XzHBQUFER4e7sbKREQKb+JE+OILKFFCu/yKuIOtYWb+/Pm0aNGC9u3bk5ycTJUqVejTpw89cp7A9j/Lly8nLCyMcuXKERMTwxtvvEFYWFiB50xLSyMtLS33dWpqKgAZGRlkZGQ4tf6c8zn7vJ5C/Xk/X+/RE/vbtg2eey4AcPDqq1nUrZtNYcvzxP6czdd7VH9FP/eVcFiWZTm9gitUsmRJAPr370/79u1Zt24d/fr1Y/z48XTu3BmAWbNmUaZMGaKjo9m5cyf/+Mc/yMzMZMOGDQQV8M+dhIQEXnnllXzj06dPJzg42LUNiUixlpXlYPDgv/Drr+W58cYjvPrqKm2OJ1JIZ86coUOHDpw8eZLQ0NBLHmtrmClRogQNGzZk1apVuWPPPvss69evZ/Xq1QV+zYEDB4iOjmbmzJnEx8fne7+gmZmoqCiOHj162R/G1crIyGDJkiXExcURGBjo1HN7AvXn/Xy9R0/r77XX/HjtNX/KlrXYsCGTatWKdj5P688VfL1H9Vd4qampVKxY8YrCjK2XmSIiIqhXr16esbp16/Lpp59e8muio6PZtm1bge8HBQUVOGMTGBjost9Irjy3J1B/3s/Xe/SE/tatg2HDzOf//KeDmjWdV48n9Odqvt6j+ivcOa+UrROgTZs2ZevWrXnGfv31V6Kjoy/6NceOHWPv3r1ERES4ujwRkSty+vS5XX4ffdTs9Csi7mNrmHnuuedYs2YNw4YN47fffmP69OlMmDCBp59+GoA//viDAQMGsHr1anbt2sXy5ctp06YNFStW5KGHHrKzdBGRXAMGmIW/VaqYZy85HHZXJFK82Bpmbr/9dubOncuMGTO48cYbee211xg9ejQdO3YEwN/fn02bNvHAAw9Qu3ZtunTpQu3atVm9ejUhISF2li4iAsCXX8K4cebzKVPgmmvsrUekOLJ1zQxA69atad26dYHvlSpVikWLFrm5IhGRK3PkCDz5pPm8Xz+45x5byxEptnTToIhIIVgW9OhhHltwww3w5pt2VyRSfCnMiIgUwkcfwbx5EBgI//43/G/bLBGxgcKMiMhV2r4d+vY1n7/+Otxyi731iBR3CjMiIlchMxM6dTK3Y991Fzz/vN0ViYjCjIjIVXjrLVi9GkJD4eOPwd/f7opERGFGROQKffcd5Dz67YMP4BL7e4qIGynMiIhcgTNnzC6/mZnQvr35XEQ8Q6HCzOTJkzlz5oyzaxER8Vh//zts3QoREWaTPO3yK+I5ChVmBg8eTHh4OE8++WSeJ16LiPiiBQtg7Fjz+eTJUL68reWIyAUKFWZSUlKYNm0aJ06cIDY2ljp16jB8+HAOHjzo7PpERGx19Ch062Y+/9vfoHlze+sRkfwKFWb8/f1p27YtSUlJ7N27l549e/Lvf/+batWq0bZtW+bNm0d2drazaxURcSvLgp494eBBqFsXhg+3uyIRKUiRFwCHhYXRtGlT7rjjDvz8/Ni0aRNdu3alZs2aLF++3AkliojYY8oUmDsXAgJg2jQoVcruikSkIIUOM4cOHeKdd97hhhtuoFmzZqSmpvLFF1+wc+dO9u/fT3x8PF26dHFmrSIibrNzJzz7rPn81Vfh1lvtrUdELq5QT81u06YNixYtonbt2vTo0YPOnTtT/rwVcaVKleL5559n1KhRTitURMRdsrLMLr+nTsFf/gIDB9pdkYhcSqHCTFhYGMnJydxxxx0XPSYiIoKdO3cWujAREbuMGAHffgshIdrlV8QbFCrMTJo0KffzP//8k5IFPC7W4XAQre0xRcTLfP89vPyy+fy996BGDXvrEZHLK9SamezsbF577TWqVKlCmTJl2LFjBwD/+Mc/8gQdERFvcvbsuV1+4+NBy/5EvEOhwszrr7/O5MmTGTFiBCVKlMgdv+mmm/jwww+dVpyIiDsNGgRbtkB4OIwfr11+RbxFocLMxx9/zIQJE+jYsSP+511Mvvnmm/nll1+cVpyIiLssXgzvv28+T0yEihXtrUdErlyhwsy+ffu47rrr8o1nZ2eTkZFR5KJERNzp2DHo2tV8/vTT0LKlreWIyFUqVJi54YYbWLlyZb7x2bNn06BBgyIXJSLiLpYFvXrBgQNQp465k0lEvEuh7mYaOnQonTp1Yt++fWRnZ5OUlMTWrVv5+OOP+eKLL5xdo4iIy0ydCnPmnNvlNzjY7opE5GoVamamTZs2zJo1i6+++gqHw8HLL7/Mli1b+Pzzz4mLi3N2jSIiLrFrFzzzjPk8IQFuu83OakSksAo1MwPQokULWrRo4cxaRETcJisLOnc2u/zecYe5k0lEvFOhwwxAeno6hw8fzveE7GrVqhWpKBERV3vnHVi5EsqUMZeaAor0t6GI2KlQf3y3bdtGt27dWLVqVZ5xy7JwOBxkZWU5pTgREVf44Qf4xz/M56NHQ82atpYjIkVUqDDTtWtXAgIC+OKLL4iIiMChnaVExEv8+afZ5TcjAx58ELp1s7siESmqQoWZjRs3smHDBurUqePsekREXGrwYPj5Z6hcGSZM0C6/Ir6gUHcz1atXj6NHjzq7FhERl/r6a3NZCWDSJKhUydZyRMRJChVmhg8fzsCBA1m+fDnHjh0jNTU1z4eIiKc5fvzcLr+9esH999tajog4UaEuM917770A3HPPPXnGtQBYRDyRZUGfPrBvH9SqZe5kEhHfUagws2zZMmfXISLiMtOnw6xZ4O9vdvktXdruikTEmQoVZmJiYpxdh4iIS+zZYx4eCfDyy/B//2dvPSLifIXeJur3339n0qRJbNmyBYfDQb169ejWrRtly5Z1Zn0iIoWWnQ1dusDJk9C4Mbz4ot0ViYgrFGoB8HfffUfNmjUZNWoUx48f5+jRo4wcOZKaNWvy/fffO7tGEZFCGTkSli83l5W0y6+I7yrUH+3nnnuOtm3bMnHiRAL+97dDZmYm3bt3p1+/fqxYscKpRYqIXK0ff4QhQ8zno0bBddfZW4+IuE6hwsx3332XJ8gABAQEMHDgQBo2bOi04kRECuPPP6FjR0hPh7ZtoXt3uysSEVcq1GWm0NBQ9uzZk2987969hISEFLkoEZGiGDIEfvrJbIo3caJ2+RXxdYUKM48++ihPPvkks2bNYu/evaSkpDBz5ky6d+/O448/7uwaRUSu2NKlZq0MmF1+w8LsrUdEXK9Ql5neeecdHA4HnTt3JjMzE8uyKFGiBL179+att95ydo0iIlfkxAlz9xJAjx7Qpo299YiIexQqzJQoUYIxY8bw5ptvsn37dizL4rrrriM4ONjZ9YmIXLGnn4aUFLPYN2d2RkR831WFmW7dul3RcR999FGhihERKawZM8yHv7+5DbtMGbsrEhF3uaowM3nyZKKjo2nQoAGWZbmqJhGRy8rKguRkBytWVOH0aQd9+pjxIUPMBnkiUnxcVZjp1asXM2fOZMeOHXTr1o0nnniC8uXLu6o2EZECJSVB376QkhIAnNsOomZNeOkl++oSEXtc1d1MY8eO5cCBAwwaNIjPP/+cqKgoHnnkERYtWqSZGhFxi6QkaNfOrI250I4d8Pnn7q9JROx11bdmBwUF8fjjj7NkyRJ+/vlnbrjhBvr06UN0dDR//PGHK2oUEQHMpaW+feFS/3bq188cJyLFR6H2mcnhcDhwOBxYlkV2drazahIRKdDKlQXPyOSwLNi71xwnIsXHVYeZtLQ0ZsyYQVxcHNdffz2bNm3igw8+YM+ePZTR7QMi4kIHDjj3OBHxDVe1ALhPnz7MnDmTatWq8de//pWZM2dSoUIFV9UmIpJHRIRzjxMR33BVYWbcuHFUq1aNGjVqkJycTHJycoHHJSUlOaU4EZHzhYebfWQutibG4YCqVeHOO91bl4jY66rCTOfOnXHoiW0iYoOffoK4uHNBxuHIuxA456+m0aNN4BGR4uOqN80TEXG3DRugRQs4dgxuvtnc0TR0aN7FwFWrmiATH29bmSJik0I9m0lExF2+/Rbuuw9SU+H222HhQihf3jxQctmyTBYs2EirVvWJjQ3QjIxIMaUwIyIe65tvoG1bOHMG7rrLbIgXGmre8/eHmBiL06f3ERNzi4KMSDFWpH1mRERc5Ysv4P77TZBp3hwWLDgXZEREzqcwIyIe55NP4KGHIC0NHnwQ5s+H4GC7qxIRT2V7mNm3bx9PPPEEFSpUIDg4mPr167Nhw4bc9y3LIiEhgcjISEqVKkWzZs3YvHmzjRWLiCtNngyPPw6ZmdChgwk2QUF2VyUinszWMHPixAmaNm1KYGAgCxYs4Oeff+bdd9+lXLlyuceMGDGCkSNH8sEHH7B+/XrCw8OJi4vj1KlT9hUuIi4xdiz89a+QnQ3du8PHH0NgoN1ViYins3UB8PDhw4mKiiIxMTF3rHr16rmfW5bF6NGjGTJkCPH/u99yypQpVK5cmenTp/PUU0+5u2QRcZERI2DQIPN5374watS5vWNERC7F1pmZ+fPn07BhQ9q3b09YWBgNGjRg4sSJue/v3LmTgwcP0rx589yxoKAgYmJiWLVqlR0li4iTWRa8/PK5IDNkiIKMiFwdW2dmduzYwb/+9S/69+/Piy++yLp163j22WcJCgqic+fOHDx4EIDKlSvn+brKlSuze/fuAs+ZlpZGWlpa7uvU1FQAMjIyyMjIcGr9Oedz9nk9hfrzfp7eo2XBoEF+jB5t7qt+/fUsBg7MJjPzyr7e0/srKl/vD3y/R/VX9HNfCYdlnb8huHuVKFGChg0b5pllefbZZ1m/fj2rV69m1apVNG3alP379xNx3pPjevTowd69e1m4cGG+cyYkJPDKK6/kG58+fTrBuh1CxGNkZ8P48TezaFENALp3/5HWrXfaXJWIeIozZ87QoUMHTp48Sehl9mWwdWYmIiKCevXq5RmrW7cun376KQDh4eEAHDx4ME+YOXz4cL7ZmhyDBw+mf//+ua9TU1OJioqiefPml/1hXK2MjAyWLFlCXFwcgT64SlH9eT9P7TEzE7p392fRIj8cDovx47Po2rUuUPeqzuOp/TmLr/cHvt+j+iu8nCsrV8LWMNO0aVO2bt2aZ+zXX38lOjoagBo1ahAeHs6SJUto0KABAOnp6SQnJzN8+PACzxkUFERQAfdxBgYGuuw3kivP7QnUn/fzpB7T0uCJJyApCQICYOpUB489VrS/ijypP1fw9f7A93tUf4U755WyNcw899xzNGnShGHDhvHII4+wbt06JkyYwIQJEwBwOBz069ePYcOGUatWLWrVqsWwYcMIDg6mQ4cOdpYuIoVw9qx5EOTChVCiBMyebR5XICJSFLaGmdtvv525c+cyePBgXn31VWrUqMHo0aPp2LFj7jEDBw7k7Nmz9OnThxMnTtCoUSMWL15MSEiIjZWLyNU6dcoEl+XLzW6+n30GcXF2VyUivsD2B022bt2a1q1bX/R9h8NBQkICCQkJ7itKRJzqxAlo1QrWroWQEPjqK/jLX+yuSkR8he1hRkR82+HD5kGR//0vlC8PixZBw4Z2VyUivkRhRkRcZt8+uPde+OUXqFwZliyBm26yuyoR8TUKMyLiEjt3wj33mP9WrQrffAO1a9tdlYj4Itufmi0ivmfrVrjrLhNkrr0WVq5UkBER11GYERGn+vFHE2RSUqBuXRNkznt+rIiI0ynMiIjTrF8PzZqZRb/160NyMkRG2l2ViPg6hRkRcYoVK8wamRMnoHFjWLYMKlWyuyoRKQ4UZkSkyBYvhpYtzcZ4sbHmrqVy5eyuSkSKC4UZESmSefOgTRvzqIL77oMvv4QyZeyuSkSKE4UZESm0GTPg4YchPd38d+5cKFXK7qpEpLhRmBGRQpk0CTp2hKws6NQJZs40D48UEXE3hRkRuWpjxkD37mBZ0KsXTJ4MAdqCU0RsojAjIldl2DDo1898/vzzMHYs+OlvEhGxkf4KEpErYlnw4oswZIh5PXQovP02OBz21iUioolhEbms7Gx47jl47z3z+u23YcAAe2sSEcmhMCMil5SVBU89ZRb8grms1Lu3vTWJiJxPYUZELiojAzp3Nncq+fnBRx9Bly52VyUikpfCjIgU6M8/4dFHYf58c6fSjBnQrp3dVYmI5KcwIyL5nDkDDz5oHksQFASffgr33293VSIiBVOYEZE8UlOhdWtYuRJKlzYzM3ffbXdVIiIXpzAjIrmOH4cWLeC776BsWfjqK2jSxO6qREQuTWFGRAA4dAji4mDTJqhQwTwJ+9Zb7a5KROTyFGZEhL174d574ddfISLCrJW54Qa7qxIRuTIKMyLF3PbtcM89sHs3VKsG33wD111nd1UiIldOjzMQKca2bIG77jJBplYts+hXQUZEvI3CjEgxtXGjCTL798ONN8KKFWZmRkTE2yjMiBRDa9ZAbCwcPQq33QbLl0N4uN1ViYgUjsKMSDGzfLlZ7Pv779C0qVkjU6GC3VWJiBSewoxIMbJgAbRqBadPm0CzaJHZT0ZExJvpbiYRH5WVBcnJDlasqELp0g6OH4cnnjAPj2zTBj75BEqWtLtKEZGiU5gR8UFJSdC3L6SkBAANGTny3HuPPgpTp0JgoG3liYg4lcKMiI9JSjJPt7asgt9v105BRkR8i9bMiPiQrCwzI3OxIONwQP/+5jgREV+hMCPiQ1auhJSUi79vWebRBStXuq8mERFXU5gR8RFZWfDZZ1d27IEDLi1FRMStFGZEvFxGBkyebB4MOWbMlX1NRIRLSxIRcSstABbxUmfPwqRJ8PbbsGePGStXDrKz4dSpgtfNOBxQtSrceadbSxURcSnNzIh4mZMn4a23oHp1+NvfTJCpXBlGjDCfJyaa4xyOvF+X83r0aPD3d2fFIiKupTAj4iWOHIGXXoLoaBg8GA4fNoFm7FjYtQv+/ncICYH4eJgzB6pUyfv1Vaua8fh4O6oXEXEdXWYS8XApKfDOOzBhgrm0BFC3rgk0jz1W8J4x8fHwwAOwbFkmCxZspFWr+sTGBmhGRkR8ksKMiIfats1cOpoyxSzyBfOE6yFDTFDxu8y8qr8/xMRYnD69j5iYWxRkRMRnKcyIeJgff4Q33zTPTsrONmPNmsGLL5qHQ164FkZEpLhTmBHxEKtXw7Bh8MUX58buv9+EmCZN7KtLRMTTKcyI2Miy4OuvTYhZvtyMORzwyCPwwgtQv76d1YmIeAeFGREbZGfDvHkmxHz3nRkLDITOnWHgQKhd2976RES8icKMiBtlZsKMGWafmJ9/NmOlSkHPnvD88xAVZW99IiLeSGFGxA3+/NM8cmD4cLMnDEDZsvDMM+Yp15Uq2VmdiIh3U5gRcaFTp2D8eHj3XTh40IxVqgT9+0Pv3ibQiIhI0SjMiLjAsWPw/vvw3ntw4oQZi4oyu/Q++SQEB9tbn4iIL1GYEXGi/fth5EgYNw5OnzZjtWubO5M6doQSJeytT0TEFynMiDjBjh1mt97EREhPN2P165s9YuLj9WBHERFXUpgRKYLNm82dSTNmQFaWGWva1DxyoGVL7dYrIuIOCjMihbB+vdkj5rPPzo21bGlmYu6807ayRESKJYUZkStkWWaX3mHDzK69YGZe4uNNiLn1VlvLExEpthRmRC7DsszzkoYNgzVrzJi/PzzxBAwaBHXr2lufiEhxpzAjchGZmTB7tnmC9aZNZiwoCLp3hwEDoHp1W8sTEZH/8bPzmyckJOBwOPJ8hIeH577ftWvXfO83btzYxorFV2RlQXKygxUrqpCc7MhdvAuQlgYTJ0KdOtChgwkyISFmFmbXLvjgAwUZERFPYvvMzA033MDXOQsQAP8L7mFt2bIliYmJua9LaKMOKaKkJPMIgZSUAKAhI0dC1armUQOHD8M778C+febYChWgXz94+mm45ho7qxYRkYuxPcwEBATkmY25UFBQ0CXfF7kaSUnQrp1ZB3O+lBSzqV2OyEizW2+PHlC6tHtrFBGRq2PrZSaAbdu2ERkZSY0aNXjsscfYsWNHnveXL19OWFgYtWvXpkePHhw+fNimSsXbZWWZGZkLg8z5/P3N7r07dpgZGQUZERHPZ+vMTKNGjfj444+pXbs2hw4d4vXXX6dJkyZs3ryZChUq0KpVK9q3b090dDQ7d+7kH//4B3fffTcbNmwgKCiowHOmpaWRlpaW+zo1NRWAjIwMMjIynFp/zvmcfV5P4Wv9JSc7/ndp6eKysqBmzUz8/Cx8oW1f+zW8kPrzfr7eo/or+rmvhMOyLvXvVPc6ffo0NWvWZODAgfTv3z/f+wcOHCA6OpqZM2cSHx9f4DkSEhJ45ZVX8o1Pnz6dYD3dr1hbsaIKI0c2vOxx/ft/x1137XNDRSIicjFnzpyhQ4cOnDx5ktDQ0Esea/uamfOVLl2am266iW3bthX4fkREBNHR0Rd9H2Dw4MF5glBqaipRUVE0b978sj+Mq5WRkcGSJUuIi4sjMDDQqef2BL7W35EjV/ZsgVat6hMTc4uLq3EPX/s1vJD6836+3qP6K7ycKytXwqPCTFpaGlu2bOHOi+wHf+zYMfbu3UtERMRFzxEUFFTgJajAwECX/UZy5bk9gbf3Z1nwr39BAZN9eTgc5q6m2NgAn3swpLf/Gl6O+vN+vt6j+ivcOa+UrQuABwwYQHJyMjt37mTt2rW0a9eO1NRUunTpwh9//MGAAQNYvXo1u3btYvny5bRp04aKFSvy0EMP2Vm2eJEjR+CBB8yt1Wlp5knWDkf+B0DmvB49Wk+4FhHxNraGmZSUFB5//HGuv/564uPjKVGiBGvWrCE6Ohp/f382bdrEAw88QO3atenSpQu1a9dm9erVhISE2Fm2eInFi+Hmm+Hzz6FECRNUNmyAOXOgSpW8x1atasYvshRLREQ8mK2XmWbOnHnR90qVKsWiRYvcWI34irQ08+DHkSPN63r1YPp0uOV/y2Di481szbJlmSxYsJFWrer75KUlEZHiwqPWzIgU1ZYt5hEEGzea1336mB19S5XKe5y/P8TEWJw+vY+YmFsUZEREvJjtm+aJOINlmc3ubrvNBJmKFWH+fPjnP/MHGRER8S2amRGvd/SoeZL1vHnmdfPmMHkyXOKmNxER8SGamRGv9vXXZpHvvHlmke/IkbBggYKMiEhxopkZ8UppaTBkCLz7rnldt65Z5Fu/vq1liYiIDRRmxOv88otZ5PvDD+Z1r14m1OhpFSIixZMuM4nXsCyYMAFuvdUEmQoV4LPPzO6+CjIiIsWXZmbEKxw7Bj16wNy55vW998KUKRAZaW9dIiJiP83MiMf75huzyHfuXAgMNPvGLFqkICMiIoZmZsRjpafDSy+Z8GJZUKeOWeTboIHdlYmIiCdRmBGPtHUrdOxonqUE8NRT5rZrrY0REZEL6TKTeBTLgg8/NIt8N2yA8uXN5aVx4xRkRESkYJqZEY9x/LhZ5JuUZF7ffTd8/HH+J1yLiIicTzMz4hGWLTOLfJOSzCLfESNgyRIFGRERuTzNzIit0tPh5ZdNeLEsqF3bLPK97Ta7KxMREW+hMCO22bbN7OT73XfmdY8eMGoUlC5tb10iIuJddJlJ3M6y4KOPzC3W330H11wDn35qdvdVkBERkaulmRlxqxMnoGdPmDPHvI6NNYt8q1a1ty4REfFempkRt0lONot858yBgAB46y2zyFdBRkREikIzM+JyGRkwdKgJL5YFtWrBv/8Nt99ud2UiIuILFGbEpX77zSzyXb/evO7WDcaMgTJl7K1LRER8hy4ziUtYFiQmQv36JsiUKwezZ8OkSQoyIiLiXJqZEac7cQJ69YJPPjGvY2Jg6lSIirK3LhER8U2amRGnWrECbrnFBJmAABg2DL75RkFGRERcRzMz4hQZGfDKK/Dmm5CdDTVrmp18/+//7K5MRER8ncKMFNn27dCxI6xda1537QrvvQchIbaWJSIixYQuM0mhWRZMmWIW+a5dC2XLwqxZZuGvgoyIiLiLZmakUH7/3SzynTXLvL7zTpg2DapVs7UsEREphjQzI1dt5UqzyHfWLPD3h9dfh2XLFGRERMQempmRAmVlQXKygxUrqlC6tIPYWHNZ6dVX4Y03zCLfa681i3wbNbK7WhERKc4UZiSfpCTo2xdSUgKAhowcCeHhEBoKv/5qjuncGd5/34yJiIjYSWFG8khKgnbtzCzM+Q4eNB/BwWYX38ces6c+ERGRC2nNjOTKyjIzMhcGmfOVLQvt27uvJhERkctRmJFcK1dCSsqljzlwwBwnIiLiKRRmJNdvv13ZcQcOuLYOERGRq6E1M8LhwzB6NIwZc2XHR0S4tBwREZGrojBTjO3dC++8AxMnwtmzZiwgADIzCz7e4YCqVc0GeSIiIp5Cl5mKoV9/hSefNPvEvPeeCTING8LcuTBjhgktDkfer8l5PXq02ShPRETEUyjMFCMbN8Kjj0KdOvDRR2YGJjYWliyBdevgwQfNbdlz5kCVKnm/tmpVMx4fb0flIiIiF6fLTMXAt9/CsGHw1Vfnxtq0gcGD4Y478h8fHw8PPADLlmWyYMFGWrWqT2xsgGZkRETEIynM+CjLgsWLTYhZscKM+fmZmZkXXoCbb7701/v7Q0yMxenT+4iJuUVBRkREPJbCjI/JzjZrX4YNg++/N2OBgdC1KwwcCNddZ2t5IiIiTqcw4yMyMszi3TffhF9+MWOlSsFTT8Hzz5s1LyIiIr5IYcbLnT0LiYkwYgTs3m3GypaFv/0Nnn0WKlWytz4RERFXU5jxUqmpMG4cjBwJhw6ZsbAw6N8fevfW06xFRKT4UJjxMkePmr1h3n8ffv/djFWrZtbDdOtmLi2JiIgUJwozXmLfPnj3XRg/Hs6cMWPXX29ur+7QwSzyFRERKY4UZjzcb7+Z9TBTpkB6uhlr0ABefBEeeki78YqIiCjMeKhNm+Ctt2DmTHO7NZhnIr34IrRokf9xAyIiIsWVwoyHWbPG3F49f/65sVatzOUkPeBRREQkP4UZD2BZsHSp2ehu6VIz5nCY5yQNHmwuK4mIiEjBFGZslJ0Nn39uQsy6dWYsIAA6dYJBg8wCXxEREbk0hRkbZGbCrFnmctLmzWasZEno3h3+/ndzq7WIiIhcGYUZN0pLg8mTzd1JO3aYsZAQePpp6NcPKle2szoRERHvpDDjBn/8ARMmwDvvwIEDZqxiRRNgnn4aypWzszoRERHvpjDjQsePwwcfwJgx5nOAKlXMpaTu3aF0aXvrExER8QUKM4WUlQXJyQ5WrKhC6dIOYmPPbWB34ACMGgX/+peZlQG47jp44QV44gkICrKvbhEREV/jZ+c3T0hIwOFw5PkIDw/Pfd+yLBISEoiMjKRUqVI0a9aMzTkrZm2UlATVq0NcXAAjRzYkLi6A6tVNeOnTB2rUgLffNkHm5pvNxne//AJPPqkgIyIi4my2z8zccMMNfP3117mv/c/bn3/EiBGMHDmSyZMnU7t2bV5//XXi4uLYunUrISEhdpRLUpLZ/8Wy8o6npJggk6NxYxgyBO6/X7v1ioiIuJLtYSYgICDPbEwOy7IYPXo0Q4YMIT4+HoApU6ZQuXJlpk+fzlNPPeXuUsnKgr598weZ8wUFwZdfwt13K8SIiIi4g+1hZtu2bURGRhIUFESjRo0YNmwY1157LTt37uTgwYM0b94899igoCBiYmJYtWrVRcNMWloaaWlpua9TU1MByMjIICMjo0i1Jic7SEm59I8sLQ0sK5PMzEskHi+R8/Mq6s/NU/l6f+D7Pao/7+frPaq/op/7Sjgs61LzDK61YMECzpw5Q+3atTl06BCvv/46v/zyC5s3b2br1q00bdqUffv2ERkZmfs1PXv2ZPfu3SxatKjAcyYkJPDKK6/kG58+fTrBwcFFqnfFiiqMHNnwssf17/8dd921r0jfS0REpDg7c+YMHTp04OTJk4SGhl7yWFvDzIVOnz5NzZo1GThwII0bN6Zp06bs37+fiIiI3GN69OjB3r17WbhwYYHnKGhmJioqiqNHj172h3E5yckO4uIuP5m1ZEkmMTEe82MttIyMDJYsWUJcXByBgYF2l+N0vt4f+H6P6s/7+XqP6q/wUlNTqVix4hWFGdsvM52vdOnS3HTTTWzbto0HH3wQgIMHD+YJM4cPH6byJbbKDQoKIqiAW4YCAwOL/IOOjYWqVWHfvoLXzTgc5v3Y2ADOW8fs9Zzxs/Nkvt4f+H6P6s/7+XqP6q9w57xStt6afaG0tDS2bNlCREQENWrUIDw8nCVLluS+n56eTnJyMk2aNLGlPn9/swEe5F/cm/N69Gh8KsiIiIh4OlvDzIABA0hOTmbnzp2sXbuWdu3akZqaSpcuXXA4HPTr149hw4Yxd+5cfvrpJ7p27UpwcDAdOnSwreb4eJgzx+zke76qVc34/268EhERETex9TJTSkoKjz/+OEePHqVSpUo0btyYNWvWEB0dDcDAgQM5e/Ysffr04cSJEzRq1IjFixfbtsdMjvh4eOABWLYskwULNtKqVX2fu7QkIiLiLWwNMzNnzrzk+w6Hg4SEBBISEtxT0FXw94eYGIvTp/cRE3OLgoyIiIhNPGrNjIiIiMjVUpgRERERr6YwIyIiIl5NYUZERES8msKMiIiIeDWFGREREfFqCjMiIiLi1RRmRERExKspzIiIiIhX86inZruC9b/HW6empjr93BkZGZw5c4bU1FSffBqq+vN+vt6j+vN+vt6j+iu8nP9v5/x//FJ8PsycOnUKgKioKJsrERERkat16tQpypYte8ljHNaVRB4vlp2dzf79+wkJCcHhcDj13KmpqURFRbF3715CQ0Odem5PoP68n6/3qP68n6/3qP4Kz7IsTp06RWRkJH5+l14V4/MzM35+flStWtWl3yM0NNQnf5PmUH/ez9d7VH/ez9d7VH+Fc7kZmRxaACwiIiJeTWFGREREvJrCTBEEBQUxdOhQgoKC7C7FJdSf9/P1HtWf9/P1HtWfe/j8AmARERHxbZqZEREREa+mMCMiIiJeTWFGREREvJrCjIiIiHi1Yh1mVqxYQZs2bYiMjMThcPDZZ5/lef/QoUN07dqVyMhIgoODadmyJdu2bctzzPbt23nooYeoVKkSoaGhPPLIIxw6dCjPMSdOnKBTp06ULVuWsmXL0qlTJ37//XcXd2e4q8c33niDJk2aEBwcTLly5Vzc1Tnu6G/Xrl08+eST1KhRg1KlSlGzZk2GDh1Kenq6T/QH0LZtW6pVq0bJkiWJiIigU6dO7N+/39XtAe7rMUdaWhr169fH4XCwceNGF3V1jrv6q169Og6HI8/HCy+84Or2APf+Gn755Zc0atSIUqVKUbFiReLj413ZGuCe/pYvX57v1y/nY/369V7fH8Cvv/7KAw88QMWKFQkNDaVp06YsW7bMKT0U6zBz+vRpbrnlFj744IN871mWxYMPPsiOHTuYN28eP/zwA9HR0dx7772cPn069+ubN2+Ow+Fg6dKlfPvtt6Snp9OmTRuys7Nzz9WhQwc2btzIwoULWbhwIRs3bqRTp04+1WN6ejrt27end+/ebukrhzv6++WXX8jOzmb8+PFs3ryZUaNGMW7cOF588UWf6A8gNjaWTz75hK1bt/Lpp5+yfft22rVr5/L+3NljjoEDBxIZGenyvnK4s79XX32VAwcO5H689NJLPtXjp59+SqdOnfjrX//Kf//7X7799ls6dOjgE/01adIkz6/dgQMH6N69O9WrV6dhw4Ze3x/A/fffT2ZmJkuXLmXDhg3Ur1+f1q1bc/DgwaI3YYllWZYFWHPnzs19vXXrVguwfvrpp9yxzMxMq3z58tbEiRMty7KsRYsWWX5+ftbJkydzjzl+/LgFWEuWLLEsy7J+/vlnC7DWrFmTe8zq1astwPrll19c3FVerurxfImJiVbZsmVd1sOluKO/HCNGjLBq1Kjh/CYuwZ39zZs3z3I4HFZ6errzG7kEV/f41VdfWXXq1LE2b95sAdYPP/zg0n4u5Mr+oqOjrVGjRrm8h8txVY8ZGRlWlSpVrA8//NA9jVyEu/4cpqenW2FhYdarr77qmkYuwlX9HTlyxAKsFStW5B6TmppqAdbXX39d5LqL9czMpaSlpQFQsmTJ3DF/f39KlCjBf/7zn9xjHA5Hns2CSpYsiZ+fX+4xq1evpmzZsjRq1Cj3mMaNG1O2bFlWrVrljlYuylk9eipX9nfy5EnKly/vosqvjKv6O378OP/+979p0qQJgYGBLuzg8pzZ46FDh+jRowdTp04lODjYTR1cmrN/DYcPH06FChWoX78+b7zxhlsuhV6Os3r8/vvv2bdvH35+fjRo0ICIiAhatWrF5s2b3dhNfq76czh//nyOHj1K165dXVf8FXBWfxUqVKBu3bp8/PHHnD59mszMTMaPH0/lypW57bbbilynwsxF1KlTh+joaAYPHsyJEydIT0/nrbfe4uDBgxw4cAAwoaR06dIMGjSIM2fOcPr0af7+97+TnZ2de8zBgwcJCwvLd/6wsDDnTK0VgbN69FSu6m/79u28//779OrVy53t5OPs/gYNGkTp0qWpUKECe/bsYd68eXa0lYezerQsi65du9KrVy+XT9lfDWf+Gvbt25eZM2eybNkynnnmGUaPHk2fPn3sai2Xs3rcsWMHAAkJCbz00kt88cUXXHPNNcTExHD8+HGv7+9CkyZNokWLFkRFRbmznXyc1Z/D4WDJkiX88MMPhISEULJkSUaNGsXChQudss5SYeYiAgMD+fTTT/n1118pX748wcHBLF++nFatWuHv7w9ApUqVmD17Np9//jllypShbNmynDx5kltvvTX3GDC/iBeyLKvAcXdyZo+eyBX97d+/n5YtW9K+fXu6d+/u7pbycHZ/f//73/nhhx9YvHgx/v7+dO7cGcvmDcKd1eP7779PamoqgwcPtrOdfJz5a/jcc88RExPDzTffTPfu3Rk3bhyTJk3i2LFjdrUHOK/HnLUXQ4YM4eGHH+a2224jMTERh8PB7Nmzvb6/86WkpLBo0SKefPJJd7eTj7P6syyLPn36EBYWxsqVK1m3bh0PPPAArVu3dso/jAOKfAYfdtttt7Fx40ZOnjxJeno6lSpVolGjRnn+Zde8eXO2b9/O0aNHCQgIoFy5coSHh1OjRg0AwsPDC1yRf+TIESpXruy2Xi7GGT16Mmf2t3//fmJjY7njjjuYMGGCu1spkDP7q1ixIhUrVqR27drUrVuXqKgo1qxZwx133OHutvJwRo9Lly5lzZo1+Z4f07BhQzp27MiUKVPc2tP5XPVnsHHjxgD89ttvVKhQweV9XIozeoyIiACgXr16uV8TFBTEtddey549e9zb0AWc/WuYmJhIhQoVaNu2rTvbuChn/Rn84osvOHHiBKGhoQCMHTuWJUuWMGXKlKLfeVfkVTc+ggsWPRXk119/tfz8/KxFixZd9JhvvvnGcjgcuYt7cxYAr127NveYNWvWeMQC4IIUpsfzedIC4IIUtr+UlBSrVq1a1mOPPWZlZmY6q+Sr4o5fvxx79uyxAGvZsmWFrLZwXNXj7t27rU2bNuV+LFq0yAKsOXPmWHv37nVmC5fkzl/Dzz//3AKs3bt3F7bcQnFVjydPnrSCgoLyLADOWSQ7fvx4p9R+JVz9a5idnW3VqFHDev75551R7lVzVX/z58+3/Pz8rFOnTuU5rnbt2tYbb7xR5LqL9czMH3/8wW+//Zb7eufOnWzcuJHy5ctTrVo1Zs+eTaVKlahWrRqbNm2ib9++PPjggzRv3jz3axITE6lbty6VKlVi9erV9O3bl+eee47rr78egLp169KyZUt69OjB+PHjAejZsyetW7fOPcbbewTYs2cPx48fZ8+ePWRlZeXu33HddddRpkwZr+5v//79NGvWjGrVqvHOO+9w5MiR3K8NDw93WW/u6m/dunWsW7eOv/zlL1xzzTXs2LGDl19+mZo1a7plVsYdPVarVi3P98z5PVmzZk2qVq3q9f2tXr2aNWvWEBsbS9myZVm/fj3PPfdc7v5BruaOHkNDQ+nVqxdDhw4lKiqK6Oho3n77bQDat2/v9f3lWLp0KTt37nTrJSZ39HfHHXdwzTXX0KVLF15++WVKlSrFxIkT2blzJ/fff3/RmyhyHPJiy5Yts4B8H126dLEsy7LGjBljVa1a1QoMDLSqVatmvfTSS1ZaWlqecwwaNMiqXLmyFRgYaNWqVct69913rezs7DzHHDt2zOrYsaMVEhJihYSEWB07drROnDjhUz126dKlwO/j6n/Zu6O/xMTEAr+HO/74uKO/H3/80YqNjbXKly9vBQUFWdWrV7d69eplpaSkuLw/d/V4oZ07d7rt1mx39LdhwwarUaNGVtmyZa2SJUta119/vTV06FDr9OnTLu/PXT1alpmJef75562wsDArJCTEuvfee/PcMuzt/VmWZT3++ONWkyZNXN7T+dzV3/r1663mzZtb5cuXt0JCQqzGjRtbX331lVN6cFiWzSv8RERERIpAdzOJiIiIV1OYEREREa+mMCMiIiJeTWFGREREvJrCjIiIiHg1hRkRERHxagozIiIi4tUUZkRERMSrKcyIiO0sy+Lee++lRYsW+d4bO3YsZcuWtf1hgiLiuRRmRMR2DoeDxMRE1q5dm/sMMzDPiBk0aBBjxoxx+jOGMjIynHo+EbGPwoyIeISoqCjGjBnDgAED2LlzJ5Zl8eSTT3LPPffwf//3f9x3332UKVOGypUr06lTJ44ePZr7tQsXLuQvf/kL5cqVo0KFCrRu3Zrt27fnvr9r1y4cDgeffPIJzZo1o2TJkkybNs2ONkXEBfRsJhHxKA8++CC///47Dz/8MK+99hrr16+nYcOG9OjRg86dO3P27FkGDRpEZmYmS5cuBeDTTz/F4XBw0003cfr0aV5++WV27drFxo0b8fPzY9euXdSoUYPq1avz7rvv0qBBA4KCgoiMjLS5WxFxBoUZEfEohw8f5sYbb+TYsWPMmTOHH374gbVr17Jo0aLcY1JSUoiKimLr1q3Url073zmOHDlCWFgYmzZt4sYbb8wNM6NHj6Zv377ubEdE3ECXmUTEo4SFhdGzZ0/q1q3LQw89xIYNG1i2bBllypTJ/ahTpw5A7qWk7du306FDB6699lpCQ0OpUaMGQL5Fww0bNnRvMyLiFgF2FyAicqGAgAACAsxfT9nZ2bRp04bhw4fnOy4iIgKANm3aEBUVxcSJE4mMjCQ7O5sbb7yR9PT0PMeXLl3a9cWLiNspzIiIR7v11lv59NNPqV69em7AOd+xY8fYsmUL48eP58477wTgP//5j7vLFBEb6TKTiHi0p59+muPHj/P444+zbt06duzYweLFi+nWrRtZWVlcc801VKhQgQkTJvDbb7+xdOlS+vfvb3fZIuJGCjMi4tEiIyP59ttvycrKokWLFtx444307duXsmXL4ufnh5+fHzNnzmTDhg3ceOONPPfcc7z99tt2ly0ibqS7mURERMSraWZGREREvJrCjIiIiHg1hRkRERHxagozIiIi4tUUZkRERMSrKcyIiIiIV1OYEREREa+mMCMiIiJeTWFGREREvJrCjIiIiHg1hRkRERHxagozIiIi4tX+H1LrA6nxjO8eAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(freqs_1,freqs_2,marker='o',color='b',)\n",
    "plt.grid()\n",
    "plt.title('Year_Money')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Money')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "94e1c8dd-dd7b-443c-a446-786a97b3577d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "69e23643-0b44-4011-b0f1-62acabc4c9ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array(range(64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c936ece7-2efc-4c08-b8ba-724e950b3d9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0727a24a-e9b0-445d-87ab-64b95b823056",
   "metadata": {},
   "outputs": [],
   "source": [
    "a.shape=(2,4,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "fc6b211b-cd3b-4378-a69c-bf36e69b3fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3,  4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11, 12, 13, 14, 15],\n",
       "        [16, 17, 18, 19, 20, 21, 22, 23],\n",
       "        [24, 25, 26, 27, 28, 29, 30, 31]],\n",
       "\n",
       "       [[32, 33, 34, 35, 36, 37, 38, 39],\n",
       "        [40, 41, 42, 43, 44, 45, 46, 47],\n",
       "        [48, 49, 50, 51, 52, 53, 54, 55],\n",
       "        [56, 57, 58, 59, 60, 61, 62, 63]]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5b95a9b4-ae43-431c-babc-1ceffab7b160",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "       104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "       117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "       130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "       143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
       "       156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
       "       169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.array(range(181))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "28e3fa90-6592-48d0-9600-5c47d704d236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.05483114, 0.10966227, 0.16449341, 0.21932454,\n",
       "       0.27415568, 0.32898681, 0.38381795, 0.43864908, 0.49348022,\n",
       "       0.54831136, 0.60314249, 0.65797363, 0.71280476, 0.7676359 ,\n",
       "       0.82246703, 0.87729817, 0.9321293 , 0.98696044, 1.04179158,\n",
       "       1.09662271, 1.15145385, 1.20628498, 1.26111612, 1.31594725,\n",
       "       1.37077839, 1.42560952, 1.48044066, 1.5352718 , 1.59010293,\n",
       "       1.64493407, 1.6997652 , 1.75459634, 1.80942747, 1.86425861,\n",
       "       1.91908974, 1.97392088, 2.02875202, 2.08358315, 2.13841429,\n",
       "       2.19324542, 2.24807656, 2.30290769, 2.35773883, 2.41256996,\n",
       "       2.4674011 , 2.52223224, 2.57706337, 2.63189451, 2.68672564,\n",
       "       2.74155678, 2.79638791, 2.85121905, 2.90605018, 2.96088132,\n",
       "       3.01571246, 3.07054359, 3.12537473, 3.18020586, 3.235037  ,\n",
       "       3.28986813, 3.34469927, 3.3995304 , 3.45436154, 3.50919268,\n",
       "       3.56402381, 3.61885495, 3.67368608, 3.72851722, 3.78334835,\n",
       "       3.83817949, 3.89301062, 3.94784176, 4.0026729 , 4.05750403,\n",
       "       4.11233517, 4.1671663 , 4.22199744, 4.27682857, 4.33165971,\n",
       "       4.38649084, 4.44132198, 4.49615312, 4.55098425, 4.60581539,\n",
       "       4.66064652, 4.71547766, 4.77030879, 4.82513993, 4.87997106,\n",
       "       4.9348022 , 4.98963334, 5.04446447, 5.09929561, 5.15412674,\n",
       "       5.20895788, 5.26378901, 5.31862015, 5.37345129, 5.42828242,\n",
       "       5.48311356, 5.53794469, 5.59277583, 5.64760696, 5.7024381 ,\n",
       "       5.75726923, 5.81210037, 5.86693151, 5.92176264, 5.97659378,\n",
       "       6.03142491, 6.08625605, 6.14108718, 6.19591832, 6.25074945,\n",
       "       6.30558059, 6.36041173, 6.41524286, 6.470074  , 6.52490513,\n",
       "       6.57973627, 6.6345674 , 6.68939854, 6.74422967, 6.79906081,\n",
       "       6.85389195, 6.90872308, 6.96355422, 7.01838535, 7.07321649,\n",
       "       7.12804762, 7.18287876, 7.23770989, 7.29254103, 7.34737217,\n",
       "       7.4022033 , 7.45703444, 7.51186557, 7.56669671, 7.62152784,\n",
       "       7.67635898, 7.73119011, 7.78602125, 7.84085239, 7.89568352,\n",
       "       7.95051466, 8.00534579, 8.06017693, 8.11500806, 8.1698392 ,\n",
       "       8.22467033, 8.27950147, 8.33433261, 8.38916374, 8.44399488,\n",
       "       8.49882601, 8.55365715, 8.60848828, 8.66331942, 8.71815055,\n",
       "       8.77298169, 8.82781283, 8.88264396, 8.9374751 , 8.99230623,\n",
       "       9.04713737, 9.1019685 , 9.15679964, 9.21163077, 9.26646191,\n",
       "       9.32129305, 9.37612418, 9.43095532, 9.48578645, 9.54061759,\n",
       "       9.59544872, 9.65027986, 9.70511099, 9.75994213, 9.81477327,\n",
       "       9.8696044 ])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=a*np.pi/180\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e272d90a-6bab-42ae-bf8a-23b2bd4c4cd5",
   "metadata": {},
   "source": [
    "# 正余弦函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8bc045ae-9cb1-4bf9-a8ef-778853959c7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import math as mt\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "af3ce1a8-68ef-4317-b622-ae1a42c97ed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t=np.array(range(361))\n",
    "t_sin=np.sin(t*np.pi/180)\n",
    "t_cos=np.cos(t*np.pi/180)\n",
    "plt.plot(t,t_sin,color='red',label='sin')\n",
    "plt.plot(t,t_cos,color='green',label='cos')\n",
    "plt.title('sin&cos')\n",
    "plt.xlabel('angle')\n",
    "plt.ylabel('value')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "480cd243-7a7d-4b2b-a929-f675852ba62e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
